/down-skilling

Code Generation

Opus: Generates well-structured code with appropriate error handling, edge cases, naming conventions, and documentation. Infers architectural patterns from context.

Haiku: Generates functional code for straightforward tasks. Common issues:

• Omits error handling unless explicitly requested
• May use inconsistent naming conventions
• Documentation/comments sparse unless requested
• Edge cases rarely handled proactively
• May not match the existing codebase style

Mitigation: Specify every non-obvious quality requirement.

Write a Python function that [task].

Requirements:
- Function signature: def process_data(input: list[dict]) -> dict
- Handle these edge cases: empty list, missing keys, non-string values
- Include type hints on all parameters and return value
- Add a docstring with Args, Returns, and Raises sections
- Use snake_case for variables
- Raise ValueError (not return None) for invalid input
- Include inline comments for non-obvious logic only

For code that must match existing style:

Match the coding style of this example:
[paste 10-20 lines of existing code]

Specifically match: indentation, naming convention, comment style,
error handling pattern, import style.

For complex code, decompose into functions: "Write function A first. Then write function B that calls A. Do not write a single monolithic function."